Intelligent Descriptor-Based Dynamic Content Generation and Delivery

One or more aspects relate, in general, to dynamic processing within a computing environment, and in particular, to improving such processing.

Currently, users create different versions of content in the form of multiple information artifacts based on the audience. For instance, different power point presentations are created for different audiences (e.g., architects vs. marketing vs. project manager vs. engineers, etc.), since varying content is wanted and/or to be provided to the different audiences. The different versions lead to version sprawl, increased maintainability, increased costs, increased memory usage and the potential of outdated information being shared. This degrades system performance and complicates the generation and delivery of content to users.

Shortcomings of the prior art are overcome, and additional advantages are provided through the provision of a method. The method includes obtaining a project-template that includes a set of role-descriptors and a task-descriptor graph. The set of role-descriptors includes one or more role-descriptors describing one or more roles relating to a project represented by the project-template and the task-descriptor graph includes a plurality of task-descriptors and one or more weights for the plurality of task-descriptors to indicate one or more relationships between the plurality of task-descriptors. An artificial intelligence application is executed on at least one computing device to dynamically generate and deliver descriptor-based content based on a request for content for a role-descriptor of the set of role-descriptors. The artificial intelligence application includes one or more artificial intelligence models trained to generate the descriptor-based content. The descriptor-based content includes content based on the role-descriptor and at least one task-descriptor of the task-descriptor graph. Data obtained based on the set of role-descriptors and the task-descriptor graph of the project template is tagged to generate tagged data. The tagged data is stored in a repository. The descriptor-based content to be delivered is generated based on the request. The generating the descriptor-based content uses the artificial intelligence application to generate from the tagged data stored in the repository the descriptor-based content. The generating the descriptor-based content is based on the role-descriptor and the task-descriptor graph. The descriptor-based content is delivered and the descriptor-based content that is delivered is specific to the role-descriptor.

Computer systems and computer program products relating to one or more aspects are also described and may be claimed herein. Further, services relating to one or more aspects are also described and may be claimed herein.

Additional features and advantages are realized through the techniques described herein. Other embodiments and aspects are described in detail herein and are considered a part of the claimed aspects.

In one or more aspects, a capability is provided to dynamically generate content to be delivered to a requester of the content. The content is dynamically generated based on a project-template defined for a project. A project may be of various types of projects, including, but not limited to, the design, implementation and/or execution of a product, such as anything that can be constructed or manufactured (e.g., computers, computer hardware, other hardware, buildings, furniture, vehicles, aircraft, watercraft, theme park rides, household goods, appliances, etc.) and/or anything that can be created, generated and/or executed (e.g., computer software, networking, information technology-related, etc.). The product may be a new product or an updated product (e.g., a new release; design change, etc.).

In accordance with one or more aspects, to facilitate dynamic generation of content, a project is defined by a project-template, which is a reusable template that defines for a project a set of role-descriptors that describes a set of roles related to the project (e.g., designer, owner, engineer, architect, marketing, advertising, finance, etc.) and includes a task-descriptor graph that includes a plurality of task-descriptors describing stages or phases of the project (e.g., ideation/cost, planning/design, build, marketing/advertising, post-delivery, etc.). In one example, the task-descriptor graph is an N dimensional graph, such as an N-dimensional directed acyclic graph, in which N is equal to or greater than 1 and there may be one or more branches from each task-descriptor node of the graph. The graph includes, for instance, the plurality of task-descriptors as a plurality of nodes of the graph and one or more weights used to determine relative importance of task-descriptors for particular role-descriptors. For instance, when one or more role-descriptors are applied to the task-descriptor graph, the weights of the task-descriptors are relative in importance to the role-descriptors. In one or more aspects, weights may be adjusted such that lower weights indicate less importance or prioritization for generating content/output (e.g., less interest to the role defined by the role-descriptor) relative to other task-descriptor nodes described in the overall graph. Conversely, higher weights imply greater importance or prioritization to the role defined by the role-descriptor relative to other task-descriptor nodes, and thus, more content is generated to be delivered. Other examples are possible.

In one example, when generating content views, where task-descriptors have low weights (as defined relative to other weights; e.g., a weight of x to y is considered low and anything over y is considered high; etc.), the generation (e.g., via a generation tool, process, etc.) seeks to reduce presentation Z-order, and conversely raise it for higher weights. Further, in one example, generation may choose not to generate content for task-descriptors whose weights are below a preselected threshold. Other examples are possible.

In one or more aspects, different content is dynamically generated for various identified role-descriptors having various levels of functional interests, knowledge levels, responsibilities and accustomed content formats. However, the individualized content that is dynamically generated is not independently stored. Instead, data related to the project is stored in one or more repositories, and the individualized content is generated from the data based on a request for a role-descriptor and delivered in a format customized for that role-descriptor. This saves on storage and the time it takes to update multiple versions of content for multiple role-descriptors.

In one or more aspects, to deliver the content in the accustomed content format, an indication of a desired format for a role-descriptor is provided. This may be accomplished in a number of ways, including but not limited to, a registration mechanism in which a role-descriptor is registered as a specific role-descriptor and context regarding the role-descriptor is provided, such as content format (and/or other context, such as particular task-descriptors it is interested in; relationships to other role-descriptors; etc.); providing a data structure that includes the role-descriptors and their contexts; using an application programming interface (API) that provides the role-descriptors and their contexts; including the information in the project-template; etc. Many mechanisms are possible for providing role-descriptors and associated context for the role-descriptors.

In one example, to generate the appropriate content for the role-descriptor in the desired form of the role-descriptor, an intelligent workflow is executed. An intelligent workflow is the orchestration of automation, artificial intelligence, analytics, and skills to fundamentally change how work is performed. In one or more aspects, an intelligent workflow is defined and used to dynamically generate content for a role-descriptor and deliver that content. The intelligent workflow executes an artificial intelligence application that includes one or more trained artificial intelligence models, and therefore, may be referred to herein as an artificial intelligence workflow. As applied to intelligent descriptor-based content delivery, in one or more aspects, the intelligent workflow dynamically generates the content to be delivered based on the role-descriptor, the project phase and/or one or more task-descriptors defined in the project-template; delivers the generated content to the requester of the content in the role-descriptor's desired format; and improves the intelligent workflow. Other examples are possible.

In one or more aspects, to dynamically generate and deliver descriptor-based content, artificial intelligence is used. The artificial intelligence used includes one or more types, fields and/or strategies, such as machine learning (e.g., uses data and algorithms to imitate the way humans learn) and/or generative artificial intelligence (e.g., one or more deep-learning models capable of generating content based on data on which they were trained), as examples. The artificial intelligence executes one or models (e.g., programs that apply algorithms(s) to data to learn, e.g., recognize patterns, make predictions and/or make decisions without human intervention). As used herein, an artificial intelligence application includes one or more models trained to generate and deliver the descriptor-based content. For instance, the artificial intelligence application executes the one or more models to generate and deliver the descriptor-based content. As examples, one model may be trained for all aspects of generating and delivering descriptor-based content or various models are used for various aspects. Many examples are possible.

One or more aspects of the present disclosure are incorporated in, performed and/or used by a computing environment. As examples, the computing environment may be of various architectures and of various types, including, but not limited to: personal computing, client-server, distributed, virtual, emulated, partitioned, non-partitioned, cloud-based, quantum, grid, time-sharing, cluster, peer-to-peer, wearable, mobile, having one node or multiple nodes, having one processor or multiple processors, and/or any other type of environment and/or configuration, etc. that is capable of performing descriptor-based content generation and delivery, generating and/or executing an intelligent workflow (or multiple workflows or processes) that performs, e.g., descriptor-based content delivery and/or performing one or more other aspects of the present disclosure. Aspects of the present disclosure are not limited to a particular architecture or environment.

Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer-readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer-readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

1 FIG. 100 150 150 150 100 101 102 103 104 105 106 101 110 120 121 111 112 113 122 150 114 123 124 125 115 104 130 105 140 141 142 143 144 One example of a computing environment to perform, incorporate and/or use one or more aspects of the present disclosure is described with reference to. In one example, a computing environmentcontains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as intelligent descriptor-based content delivery code(also referred to herein as block). In addition to block, computing environmentincludes, for example, computer, wide area network (WAN), end user device (EUD), remote server, public cloud, and private cloud. In this embodiment, computerincludes processor set(including processing circuitryand cache), communication fabric, volatile memory, persistent storage(including operating systemand block, as identified above), peripheral device set(including user interface (UI) device set, storage, and Internet of Things (IOT) sensor set), and network module. Remote serverincludes remote database. Public cloudincludes gateway, cloud orchestration module, host physical machine set, virtual machine set, and container set.

101 130 100 101 101 101 1 FIG. Computermay take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment, detailed discussion is focused on a single computer, specifically computer, to keep the presentation as simple as possible. Computermay be located in a cloud, even though it is not shown in a cloud in. On the other hand, computeris not required to be in a cloud except to any extent as may be affirmatively indicated.

110 120 120 121 110 110 Processor setincludes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitrymay be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitrymay implement multiple processor threads and/or multiple processor cores. Cacheis memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor setmay be designed for working with qubits and performing quantum computing.

101 110 101 121 110 100 150 113 Computer-readable program instructions are typically loaded onto computerto cause a series of operational steps to be performed by processor setof computerand thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer-readable program instructions are stored in various types of computer-readable storage media, such as cacheand the other storage media discussed below. The program instructions, and associated data, are accessed by processor setto control and direct performance of the inventive methods. In computing environment, at least some of the instructions for performing the inventive methods may be stored in blockin persistent storage.

111 101 Communication fabricis the signal conduction paths that allow the various components of computerto communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up busses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

112 112 101 112 101 101 Volatile memoryis any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memoryis characterized by random access, but this is not required unless affirmatively indicated. In computer, the volatile memoryis located in a single package and is internal to computer, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer.

113 101 113 113 122 150 Persistent storageis any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computerand/or directly to persistent storage. Persistent storagemay be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid state storage devices. Operating systemmay take several forms, such as various known proprietary operating systems or open source Portable Operating System Interface-type operating systems that employ a kernel. The code included in blocktypically includes at least some of the computer code involved in performing the inventive methods.

114 101 101 123 124 124 124 101 101 125 Peripheral device setincludes the set of peripheral devices of computer. Data communication connections between the peripheral devices and the other components of computermay be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion-type connections (for example, secure digital (SD) card), connections made though local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device setmay include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storageis external storage, such as an external hard drive, or insertable storage, such as an SD card. Storagemay be persistent and/or volatile. In some embodiments, storagemay take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computeris required to have a large amount of storage (for example, where computerlocally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor setis made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.

115 101 102 115 115 115 101 115 Network moduleis the collection of computer software, hardware, and firmware that allows computerto communicate with other computers through WAN. Network modulemay include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network moduleare performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network moduleare performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer-readable program instructions for performing the inventive methods can typically be downloaded to computerfrom an external computer or external storage device through a network adapter card or network interface included in network module.

102 102 WANis any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WANmay be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.

103 101 101 103 101 101 115 101 102 103 103 103 End user device (EUD)is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer), and may take any of the forms discussed above in connection with computer. EUDtypically receives helpful and useful data from the operations of computer. For example, in a hypothetical case where computeris designed to provide a recommendation to an end user, this recommendation would typically be communicated from network moduleof computerthrough WANto EUD. In this way, EUDcan display, or otherwise present, the recommendation to an end user. In some embodiments, EUDmay be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

104 101 104 101 104 101 101 101 130 104 Remote serveris any computer system that serves at least some data and/or functionality to computer. Remote servermay be controlled and used by the same entity that operates computer. Remote serverrepresents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer. For example, in a hypothetical case where computeris designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computerfrom remote databaseof remote server.

105 105 141 105 142 105 143 144 141 140 105 102 Public cloudis any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloudis performed by the computer hardware and/or software of cloud orchestration module. The computing resources provided by public cloudare typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set, which is the universe of physical computers in and/or available to public cloud. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine setand/or containers from container set. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration modulemanages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gatewayis the collection of computer software, hardware, and firmware that allows public cloudto communicate through WAN.

Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.

106 105 106 102 105 106 Private cloudis similar to public cloud, except that the computing resources are only available for use by a single enterprise. While private cloudis depicted as being in communication with WAN, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloudand private cloudare both part of a larger hybrid cloud.

1 FIG. 106 105 1 FIG. The computing environment described above is only one example of a computing environment to incorporate, perform and/or use one or more aspects of the present disclosure. Other examples are possible. For instance, in one or more embodiments, one or more of the components/modules/blocks ofare not included in the computing environment and/or are not used for one or more aspects of the present disclosure. Further, in one or more embodiments, additional and/or other components/modules/blocks may be used. Other variations are possible. Cloud computing services and/or microservices (not separately shown in): private and public clouds,are programmed and configured to deliver cloud computing services and/or microservices (unless otherwise indicated, the word “microservices” shall be interpreted as inclusive of larger “services” regardless of size). Cloud services are infrastructure, platforms, or software that are typically hosted by third-party providers and made available to users through the internet. Cloud services facilitate the flow of user data from front-end clients (for example, user-side servers, tablets, desktops, laptops), through the internet, to the provider's systems, and back. In some embodiments, cloud services may be configured and orchestrated according to as “as a service” technology paradigm where something is being presented to an internal or external customer in the form of a cloud computing service. As-a-Service offerings typically provide endpoints with which various customers interface. These endpoints are typically based on a set of APIs. One category of as-a-service offering is Platform as a Service (PaaS), where a service provider provisions, instantiates, runs, and manages a modular bundle of code that customers can use to instantiate a computing platform and one or more applications, without the complexity of building and maintaining the infrastructure typically associated with these things. Another category is Software as a Service (Saas) where software is centrally hosted and allocated on a subscription basis. SaaS is also known as on-demand software, web-based software, or web-hosted software. Four technological sub-fields involved in cloud services are: deployment, integration, on demand, and virtual private networks.

2 2 FIGS.A-B In one or more aspects, content is generated and delivered based on a role-descriptor and/or one or more task-descriptors that are related to the role-descriptor of a given project, as defined by the project-template. Instead of users creating multiple versions of content (e.g., multiple versions of artifacts, such as files, videos, presentations, power points, documents, plans, blueprints, etc.) based on the audience, which leads to many versions of content (referred to as version sprawl), increases maintainability and has the potential of sharing outdated information, the content is now dynamically generated based on the role-descriptor and/or one or more task-descriptors defined in the project-template. A single set of content of conceptually related objects is created and shared, in accordance with one or more aspects of the present disclosure. As an example, content for a project is stored in a repository, and individualized content is generated therefrom based on the role-descriptors and task-descriptors defined in the project-template generated for the project. Presentable content is dynamically generated and delivered from the set of content for various identified role-descriptors with various levels of functional interests, responsibilities or concerns, with various knowledge levels, depth and width and accustomed to having information presented in different consumption formats. In one example, generative artificial intelligence uses indicated criteria (e.g., role-descriptors, levels and consumption formats) to generate content relative to a project-template (e.g., conceptual project-template) for a given consumption format. This is further described with reference to, which depict one example of a system overview for intelligent descriptor-based content delivery.

2 2 FIGS.A-B 2 FIG.B 200 205 207 207 210 260 212 214 216 218 220 222 224 226 228 230 In one example, referring to, a system overviewincludes, for instance, a template repositoryfrom which a project-templateis selected for the given project (e.g., implementation of a product, or another project). In one example, project-templateincludes a plurality of role-descriptorsdefined for a project and a plurality of task-descriptors() created for a project. In one example, the project is implementation of a product, and the plurality of role-descriptors include, for instance, a designer role-descriptor, an owner role-descriptor, a finance role-descriptor, an analysis role-descriptor, a project manager role-descriptor, a marketing role-descriptor, one or more architect role-descriptors, one or more engineer role-descriptors, one or more supplier role-descriptorsand/or one or more other role-descriptors. The plurality of role-descriptors may be dynamically defined based on the project and may be different for different projects. Many examples are possible.

As examples, the role-descriptors may be defined by a designer and/or dynamically defined using artificial intelligence that indicates the role-descriptors based on the project. For instance, the project of product implementation (identifying the product to be implemented) is input to at least one artificial intelligence model that generates a list of roles and/or role-descriptors. The list of roles and/or role-descriptors may be used as the plurality of role-descriptors and/or the designer may select from the list of roles and/or role-descriptors and/or add to the list of roles and/or role-descriptors. Other examples are possible.

212 In one example, a designer role-descriptoris defined and it is specified, for instance, as a special role, since it can see the other role-descriptors and/or is used to perform certain actions for its role-descriptor and/or other role-descriptors. For instance, it may assist in defining other role-descriptors, which may depend on the project. Further, it may be used to add direct/indirect tags to content, add weights to artifacts to indicate importance of one type of artifact over another and/or add weights to data to indicate importance of selected data over other data, etc. As examples, a direct tag is designed by, e.g., the designer and is predefined, an indirect tag is generated by, e.g., an artificial intelligence agent based, e.g., on inspecting data in the repository, and weights may be based on, e.g., internet connection/device, whether to display a video or text, based on the importance of the content to be displayed (e.g., multiple pictures), etc. The designer role-descriptor may specify graphical adjustments to be made and/or perform transcoding to convert one encoding of an artifact to another encoding, provide filtering (e.g., public facing), indicate formats and/or reduce/summarize text. Weights may be used to indicate which of these actions are to be taken. Other examples are possible.

210 240 214 240 242 244 214 216 218 220 222 A role-descriptorhas context information associated therewith including, but not limited to, one or more role-descriptor tagsthat provide role-descriptor-based information. For instance, for owner role-descriptor, role-descriptor tagsmay include, in one example, a competition tagand a visualization tag. Additional, fewer and/or other role-descriptor tags may be assigned to owner role-descriptor. Further, other role-descriptors may have other role-descriptor tags assigned thereto. As examples, finance role-descriptorincludes the following role-descriptor tags, in one example: contracts/vendors, budget, investors (return on investment (ROI)) and/or sales projections; analysis role-descriptorincludes the following role-descriptor tags, in one example: customer utility, actual sales and/or seasonal analysis; project manager role-descriptorincludes the following role-descriptor tags, in one example: project plan, reporting and/or resourcing; marketing role-descriptorincludes the following tags, in one example: customer feedback (e.g., demographics) and/or advertising (e.g., television, radio, online, etc.). A role-descriptor may have one or more role-descriptor tags, and the example role-descriptor tags are just examples. Each role-descriptor may have additional, fewer and/or other role-descriptor tags assigned thereto. The role-descriptor tags may be defined by the designer manually or automatically based on the project, and/or dynamically assigned using artificial intelligence. For instance, one or more artificial intelligence models (e.g., machine learning models and/or other artificial intelligence models) define the role-descriptor tags based on the defined role-descriptors for the particular project. Other examples are possible.

240 In one or more aspects, a role-descriptor may have a relationshipwith one or more other role-descriptors. As such, at least a portion of the content generated for one role-descriptor may be delivered for one or more other role-descriptors to which the one role-descriptor has a role-descriptor relationship. Role-descriptor relationships may be indicated in the project-template, as part of the role-descriptor registration context or in other ways.

207 252 210 260 205 260 210 2 FIG.B In one example, for each project, project-template() includes staged informationbased on role-descriptorsand task-descriptorsdefined within the project-template for the project (e.g., implement a product). As one example, a designer/creator of the project (e.g., product to be implemented) may select a previously defined project-template from a repository (e.g., template repository). The project-template comes, e.g., with a set of task-descriptorsand/or a set of role-descriptorsthat are typically used for the project. The project-template may be dynamically modified based on input provided by, e.g., the designer. Moreover, a project-template may be created based on the project and using artificial intelligence. The created project-template may be saved in the repository for selection and use by other designers. Many examples are possible.

207 260 262 264 266 268 269 207 In one example, project-templateincludes a plurality of task-descriptors, including but not limited to, an ideation/cost task-descriptor, a planning/design task-descriptor, a build task-descriptor, a marketing/advertising task-descriptorand a post-delivery task-descriptor. A project-template (e.g., project-template) may have additional, fewer and/or other task-descriptors (and/or role-descriptors). The task-descriptors described herein are just examples and many other examples are possible. The task-descriptors for a project may be defined by a designer manually or automatically based on the project, and/or dynamically assigned using artificial intelligence. For instance, one or more artificial intelligence models (e.g., machine learning models and/or other artificial intelligence models) may be trained and/or used to generate the plurality of task-descriptors based on a given project. Further, the designer may use the artificial intelligence generated task-descriptors and/or select therefrom and/or add to those generated task-descriptors. Many examples are possible.

260 270 262 272 264 274 266 276 268 278 269 279 Each task-descriptordefined in the project-template for a particular project includes, for instance, one or more task-descriptor tagsthat suggest types of content for the task-descriptor/role-descriptor. For example, for task-descriptor(e.g., ideation/cost), task-descriptor tagsinclude, e.g., drawings, sketches and maps; for task-descriptor(e.g., planning/design), task-descriptor tagsinclude, e.g., PI/PL (program increment/program plan), head count, timeframe; for task-descriptor(e.g., build), task-descriptor tagsinclude, e.g., project plans, HLD (high-level design), LLD (low-level design); for task-descriptor(e.g., marketing, advertising), task-descriptor tagsinclude, e.g., blogs, social, marketing; and for task-descriptor(e.g., post-delivery), task-descriptor tagsinclude, e.g., reviews, feedback, satisfaction. Each task-descriptor may have additional, fewer and/or other task-descriptor tags. The task-descriptor tags described herein are just examples and many other examples are possible.

262 216 282 218 282 222 282 264 216 284 214 284 220 284 266 286 220 286 226 286 228 268 222 288 288 288 269 218 289 216 289 a b c a b c a b c a b a a b In one example, each task-descriptor may be populated in the project-template with at least one empty set for one or more role-descriptors. An empty set may be replaced by a description of content to be provided based on the role-descriptor. For example, for task-descriptor: an empty set for finance role-descriptoris replaced by cost estimates, an empty set for analysis role-descriptoris replaced by competitive analysis, and an empty set for marketing role-descriptoris replaced by consultation. As another example, for task-descriptor: an empty set for finance role-descriptoris replaced by RFI (request for information)/RFP (request for proposal), an empty set for owner role-descriptoris replaced by approvals, and an empty set for project manager role-descriptoris replaced by feasibility. For task-descriptor, in one example, sub-task-descriptors are defined including, for instance, sub-task-descriptor product managementfor project manager role-descriptor, sub-task-descriptor: engineeringfor engineer role-descriptor, and sub-task-descriptor third party supplierfor supplier role-descriptor. In another example, for task-descriptor: an empty set for marketing role-descriptoris replaced by advertising(e.g., television) and marketing strategy. Further, in one example, weights are used for advertising. In yet another example, for task-descriptor: an empty set for analysis role-descriptoris replaced by consumer surveys, and an empty set for finance role-descriptoris replaced by cost review. Many other examples are possible.

260 292 In one aspect, content, as described by the content descriptions associated with task-descriptors, is dynamically generated for a role-descriptor based on data (e.g., content) created and stored in one or more repositories(also referred to as content repositories) and may be presented as different types of artifacts, including, but not limited to, pdfs (portable document formats), images, videos, CAD (computer-aided design), plans, documents, power points, etc.

294 294 In one aspect, selected data is taggedbased, for instance, on the role-descriptor tags and/or the task-descriptor tags. For instance, considering the role-descriptor tags and/or the task-descriptor tags, data in the content repositories is reviewed and data that appears relevant to the role-descriptor tags and/or task-descriptor tags are tagged providing metadata that describes the tagged data. The tagging is performed, in one example, automatically using artificial intelligence tagging (e.g., artificial intelligence (AI)/machine learning (ML) tagging). The tagging facilitates dynamic generation of content to be delivered (e.g., presented, provided, forwarded, displayed, visualized, etc.) for a role-descriptor. As an example, the artificial intelligence is trained in how to tag the data based on the role-descriptors (e.g., role-descriptor tags) and/or task-descriptors (e.g., task-descriptor tags), and this training is continuous in that the artificial intelligence continues to learn and retrains itself based on the learning. In one example, artificial intelligence may be used to clean/standardize the tags associated with the data. Many examples are possible.

290 In one or more aspects, the content provided for a role-descriptor is dynamically generated based on the role-descriptor, task-descriptors used by the role-descriptor and the content descriptions defined for the role-descriptor and/or task-descriptors. As an example, the content is dynamically generated for delivery for the role-descriptor using artificial intelligence (e.g., one or more artificial intelligence (AI) generative transformer models). The models are trained (and retrained based on learning) based on the types of projects, role-descriptors and task-descriptors. In one example, data is created and/or suggested to train the models. Such data may be created and/or suggested by, e.g., designers, architects, engineers, employees, team leads, experts in the field, scholars, students, vendors, contractors, machine learning and/or other aspects of artificial intelligence, etc. Many sources are possible for the data.

In one or more aspects, additional, fewer and/or other role-descriptors and/or task-descriptors may be defined based on the end-audience, level of knowledge of the user, project, etc. Many examples are possible.

In one example, the content dynamically generated for a role-descriptor may be for a selected task-descriptor and/or one or more task-descriptors. In one example, the role-descriptor indicates (e.g., via an application programming interface) for which task-descriptor(s) it is requesting content. As another example, it may be based on default and/or dynamically determined (e.g., using artificial intelligence). Other examples are possible.

As described herein, artificial intelligence (e.g., generative artificial intelligence) is leveraged to intelligently bring together relative project role-descriptors, data in multiple repositories, and informational project-templates. The project-templates organize information around a project designed from start to finish and consider the lifecycle stages from data collection and tagging, through to the presentation around a scope of work (e.g., project). Based on the project and project-templates including the role-descriptors and/or task-descriptors included therein, data is collected (e.g., using tags and descriptive text that can be used to construct prompts). As an example, dynamic prompt design is used for data/information tagging and filtering using, e.g., a content assistant (e.g., artificial intelligence). In one or more aspects, data can be designed, organized and related for customized targeted presentation to end users (e.g., requesting roles) at their determined role-descriptor-based knowledge level, responsibilities, functional interests and consumption formats. A create once but share everywhere technique is provided. This is instead of creating and storing separate artifacts (e.g., files, etc. that include content, data, etc.) for each targeted role-descriptor, which increases memory usage and may lead to the use of outdated data.

150 150 113 121 124 101 104 110 120 110 In one example, to dynamically generate and deliver descriptor-based content, intelligent descriptor-based content generation and delivery code (e.g., intelligent descriptor-based content generation and delivery code) is used, in accordance with one or more aspects of the present disclosure. Intelligent descriptor-based content generation and delivery code (e.g., intelligent descriptor-based content generation and delivery code) includes code or instructions used to dynamically generate and deliver descriptor-based content, in accordance with one or more aspects of the present disclosure. The code is, e.g., computer-readable program code (e.g., instructions) in computer-readable media, e.g., storage (e.g., persistent storage, cache, storage, other storage, as examples). The computer-readable media may be part of a computer program product and the computer-readable program code may be executed by and/or using one or more computing devices (e.g., one or more computers, such as computer(s); one or more servers, such as remote server(s); one or more processors or nodes, such as processor(s) or node(s) of processor set; processing circuitry, such as processing circuitryof processor set; and/or other computing devices, etc.). Additional and/or other computing devices, computers, servers, processors, nodes and/or processing circuitry may be used to execute the code and/or portions thereof. Many examples are possible.

150 150 300 310 320 340 350 3 FIG. One example of intelligent descriptor-based content generation and delivery codeis described with reference to. In one example, intelligent descriptor-based content generation and delivery codeincludes build codeto be used to build and/or select one or more project-templates to be used in intelligent descriptor-based content delivery; data obtain codeto be used to obtain data from a variety of sources; tag codeto be used to tag the obtained data; generate codeto be used to generate the descriptor-based content to be delivered; and delivery codeto be used to deliver the generated descriptor-based content. Additional, less and/or other code may be provided and/or used in one or more aspects of the present disclosure.

150 300 350 400 101 104 110 120 110 4 FIG. 4 FIG. In one example, intelligent descriptor-based content generation and delivery codeincludes code (e.g., code-) that is used in descriptor-based content generation and delivery processing, as further described in one example with reference to.depicts one example of an intelligent workflow(and/or processing) that is executed by one or more computing devices (e.g., one or more computers, such as computer(s); one or more servers, such as remote server(s); one or more processors or nodes, such as processor(s) or node(s) of processor set; processing circuitry, such as processing circuitryof processor set; and/or other computing devices, etc.). Additional and/or other computing devices, computers, servers, processors, nodes and/or processing circuitry may be used to execute the workflow, the processing and/or portions thereof. Various options are possible.

4 FIG. 400 400 400 450 400 Referring to, in one example, intelligent workflow(also referred to as workflowor artificial intelligence workflow) dynamically generates content to be delivered for a role-descriptor. The content generated and delivered is based on the requesting role-descriptor of a project and task-descriptors defined in a project-template designed for the project. Role-descriptors relating to the project, as well as task-descriptors of the project and content descriptions, may be defined based on prompts provided by a designer and/or automatically generated by artificial intelligence based on the project. In one example, workflowuses, e.g., artificial intelligence(e.g., generative artificial intelligence, one or more large language models, foundation models, etc.) to dynamically generate prompts used in defining the project role-descriptors, task-descriptors and/or the project-templates. In one example, a project-template is predefined and obtained (e.g., provided, pulled, retrieved, etc.) by intelligent workflowand that project-template includes the set of role-descriptors and a task-descriptor graph that includes a plurality of task-descriptors. Other examples are possible.

400 410 420 450 422 424 426 428 429 In one aspect, workflowdefinesone or more role-descriptorsfor a given project. The role-descriptors may be defined by a designer and/or automatically by e.g., artificial intelligencebased on the project. In one example, prompts may be used to facilitate the defining of a role-descriptor and context information (e.g., metadata) for the role-descriptor. The context information includes, e.g., one or more role-descriptor descriptions, role-descriptor-relevant types of information(e.g., descriptions) and information, such as role-descriptor interest and detail levelsfor each, role-descriptor-relevant tagsand role-descriptor relationships, if any. In one example, a project-template is predefined that includes the set of role-descriptors and/or context information for a given project. Additional, fewer and/or other metadata may be provided. Many examples are possible.

400 430 300 440 440 440 441 442 444 446 448 Further, in one example, workflowbuilds(e.g., using build code) one or more project-templates(also referred to as templates herein) or selects from, e.g., a repository one or more project-templates. A project-templateincludes, for instance, a set of role-descriptorsand a definition of one or more project phases, each with phase information metadataincluding, for instance: project relevant information (type) descriptions, such as task-descriptors, task-descriptor specific tags/semantics; and/or a description of expected content (samples)(also referred to herein as content descriptions). Additional, fewer and/or other metadata may be provided. Many examples are possible. Project-templates may be predefined and saved and/or dynamically generated for a project based on a request. A predefined project-template includes, in one example, a set of role-descriptors and a task-descriptor graph including a set of task-descriptors typically used for the project. The set of role-descriptors and/or the set of task-descriptors may be revised based on a given instance of the project. Other examples are possible.

400 310 455 470 400 460 320 475 In one example, workfloworchestrates, using, e.g., data obtain code, the creationof data (e.g., creation, contribution, selection, provision, etc.) by content creators (e.g., designers, architects, engineers, employees, team leads, experts in the field, scholars, students, vendors, contractors, contributors, machine learning and/or other aspects of artificial intelligence, etc.), based on the project, and/or defined role-descriptors and/or task-descriptors defined in a project-template. The content is stored in one or more repositories(e.g., pdfs, images, videos, CAD, plans, documents, power points, etc.). Further, in one or more aspects, workflowtagsand/or classifies (e.g., using tag code) selected data based on the project, role-descriptors and/or task-descriptors to facilitate generating and delivering content for a role-descriptor. In one example, artificial intelligence (e.g., generative artificial intelligence) may be used to perform the classification and/or tagging with or without input from content creators. This is a dynamic process that changes, as the artificial intelligence models learn of the role-descriptors and/or task-descriptors. The tags may be stored, for instance, in a metadata storeand are used to tag data stored in the repository.

400 462 464 466 400 320 In one example, workflowdefines task-descriptor tags(also referred to as project relevant tags/classifications), which may include, for instance, descriptions of content/types, such as videos, blueprints, models, estimates, annotations, etc.; and/or other project-related and/or task-descriptor-based tags, which may have subjects of, e.g., classifications, geolocations, timestamps, as examples. In one or more aspects, one or more of the task-descriptor tags and/or descriptions may be generated using artificial intelligence. For instance, workflowuses tag codeto tag data pertaining to the tags.

400 490 340 400 472 475 480 495 400 490 450 495 In one example, workflowdynamically generatescontent (e.g., using generate code) to be delivered for a role-descriptor. In one example, workflowuses, e.g., a query engine, metadata (e.g., tags) in metadata storeand/or generative AI data (data generated using generative AI)to dynamically generatecontent to be delivered. Workfloworchestratesusing, e.g., artificial intelligencedynamic generation of contentto be provided for the role-descriptor.

400 350 Workflowdelivers (e.g., presents, provides, forwards, displays, etc.) using, for instance, deliver codethe dynamically generated content. In one example, the generated content is provided to a requester for the role-descriptor via one or more networks.

In one example, the content generated and delivered for a role-descriptor is descriptor-based in that it takes into account the role-descriptor and one or more task-descriptors associated, via, e.g., a project-template, with the role-descriptor. In one example, the task-descriptors have weights assigned thereto, which are used in one or more examples to generate and deliver content for the role-descriptor. For instance, when one or more role-descriptors are applied to the task-descriptor graph, the weights of the task-descriptors are relative in importance to the role-descriptors. In one or more aspects, weights may be adjusted such that lower weights indicate less importance or prioritization for generating content/output (e.g., less interest to the role defined by the role-descriptor) relative to other task-descriptor nodes described in the overall graph. Conversely, higher weights imply greater importance or prioritization to the role defined by the role-descriptor relative to other task-descriptor nodes, and thus, more content is generated to be delivered.

262 264 269 266 268 Therefore, if, for instance, the role-descriptor is finance, then weights are used to show that task-descriptors of, e.g., ideation/cost (e.g., task descriptor), planning and design (e.g., task descriptor) and post-delivering (e.g., task descriptor) are of greater importance and thus, have, e.g., a higher weight relative to weights assigned to other task-descriptors (e.g., task-descriptorsand). Other examples are possible.

282 262 a In other examples, weights may be used to indicate which type of content generated for a task-descriptor and for a role-descriptor is more relevant than other content. For instance, if other content besides, e.g., cost estimates (e.g., cost estimates) is generated for task-descriptor ideation/cost (e.g., task-descriptor), then weights may be used to define relative importance for content generation and/or delivery of the cost estimates versus the other content, etc. Many examples are possible.

Although various capabilities of an intelligent workflow are described herein, in other embodiments, an intelligent workflow may include additional, fewer and/or other capabilities. The capabilities/aspects described herein are just examples. Further, one or more aspects may be performed by processing that may or may not utilize an intelligent workflow.

Described above is one example of an intelligent workflow. One or more aspects of the intelligent workflow and/or intelligent descriptor-based generation content and delivery processing may use artificial intelligence including, machine learning. For instance, machine learning and/or other artificial intelligence may be used to train the workflow and/or content generation/delivery process(es), execute the workflow and/or content generation/deliver process(es), and/or perform other tasks. A system is trained to perform analyses and learn from input data and/or choices made.

5 FIG. 500 510 520 515 530 One example of a machine learning training system is described with reference to. In one or more aspects, a machine learning training systemmay be utilized to perform cognitive analyses of various inputs, including input data, data from one or more sources, repositories, data structures and/or other data. The data may include information for a project, information related to role-descriptors and/or task-descriptors of a project, information of one or more project-templates, etc. Training data utilized to train the model in one or more embodiments of the present disclosure includes, for instance, data that pertains to one or more events, such as natural language processing data, data being processed; data that pertains to projects, project-templates, role-descriptors and/or task-descriptors; data relating to devices, including monitors, sensors, environmental devices, etc.; data obtained from the devices; data obtained from exogenous sources (e.g., project information, role-descriptor information, task-descriptor information, etc.); actions that have been taken; and/or available resources; etc. The program code in embodiments of the present disclosure performs a cognitive analysis to generate one or more training data structures, including algorithms utilized by the program code to predict states of a given event (e.g., content generation/delivery, etc.). Machine learning (ML) solves problems that are not solved with numerical means alone. In this ML-based example, program code extracts various attributes from ML training data(e.g., historical attribute data collected from various data sources relevant to the event (e.g., descriptor-based content generation/delivery for a project)), which may be resident in one or more databasescomprising event or descriptor-related data and general data. Attributesare utilized to develop a predictor function, h(x), also referred to as a hypothesis, which the program code utilizes as a machine learning model.

510 540 530 530 550 510 530 In identifying various event states, features, attribute similarities, constraints and/or behaviors indicative of states in the ML training data, the program code can utilize various techniques to identify attributes in an embodiment of the present disclosure. Embodiments of the present disclosure utilize varying techniques to select attributes (data attributes, elements, patterns, features, constraints, distribution, etc.), including but not limited to, diffusion mapping, principal component analysis, recursive feature elimination (a brute force approach to selecting attributes), and/or a Random Forest, to select the attributes related to various events. The program code may utilize a machine learning algorithmto train the machine learning model(e.g., the algorithms utilized by the program code), including providing weights for the conclusions, so that the program code can train the predictor functions that comprise the machine learning model. The conclusions may be evaluated by a quality metric. By selecting a diverse set of ML training data, the program code trains the machine learning modelto identify and weight various attributes (e.g., data attributes, features, patterns, constraints, distributions, etc.) that correlate to various states of an event.

The model generated by the program code is self-learning as the program code updates the model based on active event feedback, as well as from the feedback received from data related to the event. For example, when the program code determines that there is a constraint, event, similarity or pattern (e.g., data attribute, record attribute similarity, query pattern, data distribution, search terms distribution, etc.) that was not previously predicted by the model, the program code utilizes a learning agent to update the model to reflect the state of the event, in order to improve predictions in the future. Additionally, when the program code determines that a prediction is incorrect, either based on receiving user feedback through an interface or based on monitoring related to the event, the program code updates the model to reflect the inaccuracy of the prediction for the given period of time. Program code comprising a learning agent cognitively analyzes the data deviating from the modeled expectations and adjusts the model to increase the accuracy of the model, moving forward.

In one or more embodiments, program code, executing on one or more processors, utilizes an existing cognitive analysis tool or agent (now known or later developed) to tune the model, based on data obtained from one or more data sources. In one or more embodiments, the program code interfaces with application programming interfaces to perform a cognitive analysis of obtained data. Specifically, in one or more embodiments, certain application programming interfaces comprise a cognitive agent (e.g., learning agent) that includes one or more programs, including, but not limited to, natural language classifiers, a retrieve and rank service that can surface the most relevant information from a collection of documents, concepts/visual insights, trade off analytics, document conversion, and/or relationship extraction. In an embodiment, one or more programs analyze the data obtained by the program code across various sources utilizing one or more of a natural language classifier, retrieve and rank application programming interfaces, and trade off analytics application programming interfaces. An application programming interface can also provide audio related application programming interface services, in the event that the collected data includes audio, which can be utilized by the program code, including but not limited to natural language processing, text to speech capabilities, and/or translation.

In one or more embodiments, the program code utilizes a neural network to analyze event-related data to generate the model utilized to predict the state of a given event at a given time. Neural networks are biologically-inspired programming paradigms, which enable a computer to learn and solve artificial intelligence problems. This learning is referred to as deep learning, which is a subset of machine learning, an aspect of artificial intelligence, and includes a set of techniques for learning in neural networks. Neural networks, including modular neural networks, are capable of pattern recognition with speed, accuracy, and efficiency, in situations where data sets are multiple and expansive, including across a distributed network, including but not limited to, cloud computing systems. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to identify patterns (or similarities) in data (i.e., neural networks are non-linear statistical data modeling or decision-making tools). In general, program code utilizing neural networks can model complex relationships between inputs and outputs and identify patterns in data. Because of the speed and efficiency of neural networks, especially when parsing multiple complex data sets, neural networks and deep learning provide solutions to many problems in multiple source processing, which the program code in one or more embodiments accomplishes when obtaining data and generating a model for predicting states of a given event.

As described herein, an intelligent workflow and/or content generation/delivery process(es) are provided that use artificial intelligence to perform descriptor-based content generation and delivery. As an example, the intelligent workflow and/or content generation/delivery process(es) is used in the design and implementation of a product release, including, but not limited to, the manufacturing of a physical product or informational technology product. The information modeling considers different role-descriptors for different roles, such as product owner, architect, engineer, marketing, consumer, etc. Each of these role-descriptors uses different information presented in different formats.

Another example of using an intelligent workflow and/or content generation/delivery process(es) to generate and deliver selective content for a role-descriptor is the design and implementation of a theme park ride. The information modeling considers different role-descriptors that use different content at different levels of detail used for different goals at different times of the overall design, development and delivery lifecycle of the project. The information targets roles defined by role-descriptors with various interests and knowledge including, for example, business owners, marketing; architects, engineers, contractors; and/or media, customer/consumer, etc. Each of these role-descriptors uses different content presented in different formats.

Another example of using an intelligent workflow and/or content generation/delivery process(es) to generate and deliver content for a role-descriptor is the design and implementation of a cookbook or other type of book that describes recipes or procedures. There would be different role-descriptors, depending on the end user of the book, and a recipe, as an example, could be tailored to a home cook, restaurant chef, dietary habits, marketing, customer/consumer feedback, etc. Each of these role-descriptors uses different content presented in different formats.

In one or more aspects, a generalized model is provided for artifact production with a descriptor-based modelling aspect. The concept model is visually represented (e.g., pptx (power point presentation), mural, website, etc.). An artifact is produced that includes valuable information that pertains to a specific role-descriptor and viewable by a particular role-descriptor. A presentation is generated that is viewable by a specific role defined by a specific role-descriptor and it includes only the information that the role-descriptor is to view. In one or more aspects, objects that are specific to a particular role-descriptor are embedded and the same artifact displays a different object when the role-descriptor changes. A generalized model is generated for artifact production based on a specific role-descriptor. In one or more aspects, role-descriptors, role-descriptor descriptions and semantic tags are leveraged by artificial intelligence as part of descriptor-based content generation and delivery. Artificial intelligence is used to dynamically render the content relative to information project-templates or use project-scoped tagged content repositories.

One or more aspects are tied to computer technology and facilitate processing within a computer, improving performance thereof. In one or more aspects, technical fields of computing and artificial intelligence are improved. For instance, the generation and/or execution of intelligent workflows and content generation/delivery processes is facilitated and/or improved. Processing is facilitated by dynamically changing the workflow and/or processes. The dynamic generation and delivery of descriptor-based content saves on memory/storage requirements by not requiring storing of each possible content.

In one or more aspects, a tailored intelligent workflow is generated to generate and deliver content specific to a particular role-descriptor and task-descriptors to be performed for that role-descriptor for a project. In one or more aspects, continuous feedback and improvement are performed. The experiences are used to improve, e.g., the large language model so that the next role-descriptor/stage is improved based on experience. Recommended training is provided so the next iteration is improved and/or faster.

In one or more aspects, a create once, share anywhere technique is provided, in which time is saved, version sprawl is reduced, out-of-date information is less likely to be shared, and/or the maintaining of updated information is less complex. Instead, artificial intelligence is used to leverage descriptor-based project-templates for repeatable management and presentation of information. An individual managing a project or service that is to share and coordinate information to a wide and varied set of entities may: leverage artificial intelligence against a managed data set, across the life cycle of the project to suggest and create content for descriptor-based information project-templates, based upon role-descriptor and consumption format; dynamically create prompts, based upon defined project role-descriptors, as well as classified/tagged project-relevant content sourced from multiple project repositories; manage workflows, represented as project-templates, using orchestration to create and deliver for/to different consumption formats. The templated information mapping templates across the different industries or task-descriptor specific workflows is re-usable.

In one or more aspects, a capability of designing conceptually related objects is provided that includes, for instance, collecting and tagging data based on e.g., role-descriptors, in which tagged data is stored in, e.g., a conceptual project-template repository; filtering the tagged data using a machine learning model, in which the machine learning model standardizes a set of tags associated with the tagged data; building, e.g., a conceptual project-template, in which the tagged data is assigned to one or more concepts based on a task-descriptor and each of the one or more concepts is comprised of role-descriptors, in which the conceptual project-template includes the relationships between the role-descriptors; and generating a visual representation of a set of problems corresponding to the conceptual template.

In one or more aspects, descriptor-based descriptions and interests (and tags) are utilized to relate interests in concepts (e.g., task-descriptors) (defined for against a project-template or instance of a project-template of relational concepts) along with selected artifacts/views those role-descriptors expect or prefer. These descriptor-to-concept relationships are used to suggest/provide content relative to concepts which can also be used to generate views of those relational concepts. Information collected is based on each role-descriptor and combined to generate a full document (e.g., content to be delivered). Each individual user fills in only the data that is relevant to their role-descriptor. Content is customized and displayed to users based on role-descriptors.

One or more aspects generate relevant content to a set of relational concepts (e.g., task-descriptors), against a larger project. In one or more aspects, data is applied to a set of relational concepts that itself provides additional inputs to the overall generating and presenting against a larger project context.

The computing environments described herein are only examples of computing environments that can be used. One or more aspects of the present disclosure may be used with many types of environments. Each computing environment is capable of being configured to include and/or use one or more aspects of the present disclosure. For instance, each may be configured to provide, process and/or use an intelligent workflow, to dynamically generate and deliver descriptor-based content and/or perform one or more other aspects of the present disclosure.

In addition to the above, one or more aspects may be provided, offered, deployed, managed, serviced, etc. by a service manager who offers management of customer environments. For instance, the service manager can create, maintain, support, etc. computer code and/or a computer infrastructure that performs one or more aspects for one or more customers. In return, the service manager may receive payment from the customer under a subscription and/or fee agreement, as examples. Additionally, or alternatively, the service manager may receive payment from the sale of advertising content to one or more third parties.

In one aspect, an application may be deployed for performing one or more embodiments. As one example, the deploying of an application comprises providing computer infrastructure operable to perform one or more embodiments.

As a further aspect, a computing infrastructure may be deployed comprising integrating computer readable code into a computing system, in which the code in combination with the computing system is capable of performing one or more embodiments.

As yet a further aspect, a process for integrating computing infrastructure comprising integrating computer readable code into a computer system may be provided. The computer system comprises a computer readable medium, in which the computer medium comprises one or more embodiments. The code in combination with the computer system is capable of performing one or more embodiments.

Although various embodiments are described above, these are only examples. For example, other projects, project-templates, role-descriptors and/or task-descriptors may be defined. Many variations are possible.

Various aspects and embodiments are described herein. Further, many variations are possible without departing from a spirit of aspects of the present disclosure. It should be noted that, unless otherwise inconsistent, each aspect or feature described and/or claimed herein, and variants thereof, may be combinable with any other aspect or feature.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising”, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below, if any, are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of one or more embodiments has been presented for purposes of illustration and description but is not intended to be exhaustive or limited to in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain various aspects and the practical application, and to enable others of ordinary skill in the art to understand various embodiments with various modifications as are suited to the particular use contemplated.